class_docstr: Categorical distribution parametrized by logits.
code: def entropy(self, inputs):
      log_probs = tl.LogSoftmax()(inputs)
      probs = jnp.exp(log_probs)
      return -jnp.sum(probs * log_probs, axis=-1)
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 6 for calling the Categorical obj's underlying member method code with example usage: obj.entropy(inputs) and returns: unknown